Machine learning reveals hidden components of x-ray pulses

(Nanowerk News) Ultrafast pulses from X-ray lasers reveal how atoms move at timescales of a femtosecond. That’s a quadrillionth of a second. However, measuring the properties of the pulses themselves is challenging. While determining a pulse’s maximum strength, or ‘amplitude,’ is straightforward, the time at which the pulse reaches the maximum, or ‘phase,’ is often hidden.
A new study trains neural networks to analyze the pulse to reveal these hidden sub-components (Optics Express, "Recovering the phase and amplitude of X-ray FEL pulses using neural networks and differentiable models").
Physicists also call these sub-components ‘real’ and ‘imaginary.’ Starting from low-resolution measurements, the neural networks reveal finer details with each pulse, and they can analyze pulses millions of times faster than previous methods.
An X-ray pulse (white line) is built from ‘real’ and ‘imaginary’ components (red and blue dashes) that determine quantum effects
An X-ray pulse (white line) is built from ‘real’ and ‘imaginary’ components (red and blue dashes) that determine quantum effects. A neural network analyzes low resolution measurements (black shadow) to reveal the high-resolution pulse and its components. (Image: SLAC National Accelerator Laboratory)
The new analysis method is up to three times more accurate and millions of times faster than existing methods. Knowing the components of each X-ray pulse leads to better, crisper data. This will expand the science possible using ultrafast X-ray lasers, including fundamental research in chemistry, physics, and materials science and applications in fields such as quantum computing.
For example, the additional pulse information could enable simpler and higher-resolution time-resolved experiments, reveal new areas of physics, and open the door to new investigations of quantum mechanics.
The neural network approach used here could also have broad applications in X-ray and accelerator science, including learning the shape of proteins or the properties of an electron beam.
Characterizations of system dynamics are important applications for X-ray free-electron lasers (XFELs), but measuring the time-domain properties of the X-ray pulses used in those experiments is a long-standing challenge. Diagnosing the properties of each individual XFEL pulse could enable a new class of simpler and potentially higher-resolution dynamics experiments.
This research by scientists from SLAC National Accelerator Laboratory and the Deutsches Elektronen-Synchrotron is a step toward that goal. The new approach trains neural networks, a form of machine learning, to combine low-resolution measurements in both the time and frequency domains and recover the properties of X-ray pulses at high resolution.
The model-based ‘physics-informed’ neural-network architecture can be trained directly on unlabeled experimental data and is fast enough for real-time analysis on the new generation of megahertz XFELs.
Critically, the method also recovers the phase, opening the door to coherent-control experiments with XFELs, shaping the intricate motion of electrons in molecules and condensed-matter systems.
Source: SLAC National Accelerator Laboratory
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